Search NASA⌕ Search
Home/Archive/DOE OSTI

DOE OSTI · 1440982

Electronic inverter assembly

Singh, Brij N.·Schmit, Christopher J.
View original source ↗Read source document ↗Export metadata ↓

Abstract

A first driver portion comprises a set of first components mounted on or associated with a first circuit board. A second circuit board is spaced apart from the first circuit board. A second driver portion comprises a set of second components mounted on or associated with the second circuit board, where the first driver portion and the second driver portion collectively are adapted to provide input signals to the control terminal of each semiconductor switch of an inverter. A first edge connector is mounted on the first circuit board. A second edge connector is mounted on the second circuit board. An interface board has mating edges that mate with the first edge connector and the second edge connector.

Explore related subjects

42 ENGINEERING

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Singh, Brij N., Schmit, Christopher J.. 2018-05-22. Electronic inverter assembly. https://www.osti.gov/biblio/1440982

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

DOE OSTI2026

Large language models for transportation research: Methodologies, state of the art, and future opportunities

The rapid rise of large language models (LLMs) is transforming transportation research, with significant advancements emerging between 2023 and 2025, a period marked by the inception and swift growth of adopting and adapting LLMs for various transportation applications. Despite these significant advancements, however, a systematic review and synthesis of the existing literature remains lacking. This paper aims to fill this gap by providing a comprehensive review of the methodologies and applications of LLMs in transportation. We explore key applications, including autonomous driving, travel behavior prediction, and general transportation-related queries, alongside LLM methodologies such as zero- or few-shot learning, prompt engineering, and fine-tuning. From the review, critical research gaps are identified. From the methodological perspective, many of the research limitations can be addressed by integrating LLMs with existing tools and refining LLM architectures. From the application perspective, research opportunities for LLMs to address various transportation challenges are also explored. By synthesizing these findings, this review not only presents the state-of-the-art LLM adoption and adaptation in transportation, but also proposes future research directions as well as insights and recommendations for policymakers and practitioners, paving the way for greater LLM-driven research innovations in transportation in the future.

42 ENGINEERING↗